code: def forward(self, inputs):
      # shape notation
      # i_s: input_size
      # s: size
      # u: units
      # b_s: batch_size
  
      # inputs shape [b_s, i_s]
      hidden = self.fc_layer(inputs)  # [b_s, u]
      hidden = self.batch_norm(hidden)  # [b_s, u]
      if self.apply_glu:
          hidden = nn.functional.glu(hidden, dim=-1)  # [bs, s]
      return hidden
  
imports: from typing import List, Optional, Tuple
  import torch
  import torch.nn
  from ludwig.modules.normalization_modules import GhostBatchNormalization
  from ludwig.utils.entmax import Entmax15, EntmaxBisect, Sparsemax
  from ludwig.utils.torch_utils import LudwigModule
label_desc: Use image node_id 2 for calling the FeatureBlock obj's underlying member method code with example usage: obj.forward(inputs) and returns: hidden